dill package documentation¶

dill: serialize all of Python¶

About Dill¶

dill extends Python’s pickle module for serializing and de-serializing Python objects to the majority of the built-in Python types. Serialization is the process of converting an object to a byte stream, and the inverse of which is converting a byte stream back to a Python object hierarchy.

dill provides the user the same interface as the pickle module, and also includes some additional features. In addition to pickling Python objects, dill provides the ability to save the state of an interpreter session in a single command. Hence, it would be feasible to save an interpreter session, close the interpreter, ship the pickled file to another computer, open a new interpreter, unpickle the session and thus continue from the ‘saved’ state of the original interpreter session.

dill can be used to store Python objects to a file, but the primary usage is to send Python objects across the network as a byte stream. dill is quite flexible, and allows arbitrary user defined classes and functions to be serialized. Thus dill is not intended to be secure against erroneously or maliciously constructed data. It is left to the user to decide whether the data they unpickle is from a trustworthy source.

dill is part of pathos, a Python framework for heterogeneous computing. dill is in active development, so any user feedback, bug reports, comments, or suggestions are highly appreciated. A list of issues is located at https://github.com/uqfoundation/dill/issues, with a legacy list maintained at https://uqfoundation.github.io/project/pathos/query.

Major Features¶

dill can pickle the following standard types:

none, type, bool, int, float, complex, bytes, str,

tuple, list, dict, file, buffer, builtin,

Python classes, namedtuples, dataclasses, metaclasses,

instances of classes,

set, frozenset, array, functions, exceptions

dill can also pickle more ‘exotic’ standard types:

functions with yields, nested functions, lambdas,

cell, method, unboundmethod, module, code, methodwrapper,

methoddescriptor, getsetdescriptor, memberdescriptor, wrapperdescriptor,

dictproxy, slice, notimplemented, ellipsis, quit

dill cannot yet pickle these standard types:

frame, generator, traceback

dill also provides the capability to:

save and load Python interpreter sessions

save and extract the source code from functions and classes

interactively diagnose pickling errors

Current Release¶

The latest released version of dill is available from:

https://pypi.org/project/dill

dill is distributed under a 3-clause BSD license.

Development Version¶

You can get the latest development version with all the shiny new features at:

https://github.com/uqfoundation

If you have a new contribution, please submit a pull request.

Installation¶

dill can be installed with pip:

$ pip install dill

To optionally include the objgraph diagnostic tool in the install:

$ pip install dill[graph]

For windows users, to optionally install session history tools:

$ pip install dill[readline]

Requirements¶

dill requires:

python (or pypy), >=3.7

setuptools, >=42

Optional requirements:

objgraph, >=1.7.2

pyreadline, >=1.7.1 (on windows)

Basic Usage¶

dill is a drop-in replacement for pickle. Existing code can be updated to allow complete pickling using:

>>> import dill as pickle

or:

>>> from dill import dumps, loads

dumps converts the object to a unique byte string, and loads performs the inverse operation:

>>> squared = lambda x: x**2
>>> loads(dumps(squared))(3)
9

There are a number of options to control serialization which are provided as keyword arguments to several dill functions:

with protocol, the pickle protocol level can be set. This uses the same value as the pickle module, DEFAULT_PROTOCOL.
with byref=True, dill to behave a lot more like pickle with certain objects (like modules) pickled by reference as opposed to attempting to pickle the object itself.
with recurse=True, objects referred to in the global dictionary are recursively traced and pickled, instead of the default behavior of attempting to store the entire global dictionary.
with fmode, the contents of the file can be pickled along with the file handle, which is useful if the object is being sent over the wire to a remote system which does not have the original file on disk. Options are HANDLE_FMODE for just the handle, CONTENTS_FMODE for the file content and FILE_FMODE for content and handle.
with ignore=False, objects reconstructed with types defined in the top-level script environment use the existing type in the environment rather than a possibly different reconstructed type.

The default serialization can also be set globally in dill.settings. Thus, we can modify how dill handles references to the global dictionary locally or globally:

>>> import dill.settings
>>> dumps(absolute) == dumps(absolute, recurse=True)
False
>>> dill.settings['recurse'] = True
>>> dumps(absolute) == dumps(absolute, recurse=True)
True

dill also includes source code inspection, as an alternate to pickling:

>>> import dill.source
>>> print(dill.source.getsource(squared))
squared = lambda x:x**2

To aid in debugging pickling issues, use dill.detect which provides tools like pickle tracing:

>>> import dill.detect
>>> with dill.detect.trace():
>>>     dumps(squared)
┬ F1: <function <lambda> at 0x7fe074f8c280>
├┬ F2: <function _create_function at 0x7fe074c49c10>
│└ # F2 [34 B]
├┬ Co: <code object <lambda> at 0x7fe07501eb30, file "<stdin>", line 1>
│├┬ F2: <function _create_code at 0x7fe074c49ca0>
││└ # F2 [19 B]
│└ # Co [87 B]
├┬ D1: <dict object at 0x7fe0750d4680>
│└ # D1 [22 B]
├┬ D2: <dict object at 0x7fe074c5a1c0>
│└ # D2 [2 B]
├┬ D2: <dict object at 0x7fe074f903c0>
│├┬ D2: <dict object at 0x7fe074f8ebc0>
││└ # D2 [2 B]
│└ # D2 [23 B]
└ # F1 [180 B]

With trace, we see how dill stored the lambda (F1) by first storing _create_function, the underlying code object (Co) and _create_code (which is used to handle code objects), then we handle the reference to the global dict (D2) plus other dictionaries (D1 and D2) that save the lambda object’s state. A # marks when the object is actually stored.

More Information¶

Probably the best way to get started is to look at the documentation at http://dill.rtfd.io. Also see dill.tests for a set of scripts that demonstrate how dill can serialize different Python objects. You can run the test suite with python -m dill.tests. The contents of any pickle file can be examined with undill. As dill conforms to the pickle interface, the examples and documentation found at http://docs.python.org/library/pickle.html also apply to dill if one will import dill as pickle. The source code is also generally well documented, so further questions may be resolved by inspecting the code itself. Please feel free to submit a ticket on github, or ask a question on stackoverflow (@Mike McKerns). If you would like to share how you use dill in your work, please send an email (to mmckerns at uqfoundation dot org).

Citation¶

If you use dill to do research that leads to publication, we ask that you acknowledge use of dill by citing the following in your publication:

M.M. McKerns, L. Strand, T. Sullivan, A. Fang, M.A.G. Aivazis,
"Building a framework for predictive science", Proceedings of
the 10th Python in Science Conference, 2011;
http://arxiv.org/pdf/1202.1056

Michael McKerns and Michael Aivazis,
"pathos: a framework for heterogeneous computing", 2010- ;
https://uqfoundation.github.io/project/pathos

Please see https://uqfoundation.github.io/project/pathos or http://arxiv.org/pdf/1202.1056 for further information.

exception PickleError¶: Bases: Exception

exception PickleWarning¶: Bases: Warning, PickleError

class Pickler(file, *args, **kwds)¶

Bases: _Pickler

python’s Pickler extended to interpreter sessions

This takes a binary file for writing a pickle data stream.

The optional protocol argument tells the pickler to use the given protocol; supported protocols are 0, 1, 2, 3, 4 and 5. The default protocol is 4. It was introduced in Python 3.4, and is incompatible with previous versions.

Specifying a negative protocol version selects the highest protocol version supported. The higher the protocol used, the more recent the version of Python needed to read the pickle produced.

The file argument must have a write() method that accepts a single bytes argument. It can thus be a file object opened for binary writing, an io.BytesIO instance, or any other custom object that meets this interface.

If fix_imports is True and protocol is less than 3, pickle will try to map the new Python 3 names to the old module names used in Python 2, so that the pickle data stream is readable with Python 2.

If buffer_callback is None (the default), buffer views are serialized into file as part of the pickle stream.

If buffer_callback is not None, then it can be called any number of times with a buffer view. If the callback returns a false value (such as None), the given buffer is out-of-band; otherwise the buffer is serialized in-band, i.e. inside the pickle stream.

It is an error if buffer_callback is not None and protocol is None or smaller than 5.

_session = False¶

dispatch: Dict[type, Callable[[dill._dill.Pickler, Any], None]]¶

The dispatch table, a dictionary of serializing functions used by Pickler to save objects of specific types. Use pickle() or register() to associate types to custom functions.

dump(obj)¶: Write a pickled representation of obj to the open file.

save(obj, save_persistent_id=True)¶

settings = {'byref': False, 'fmode': 0, 'ignore': False, 'protocol': 4, 'recurse': False}¶

exception PicklingError¶: Bases: PickleError

exception PicklingWarning¶: Bases: PickleWarning, PicklingError

class Unpickler(*args, **kwds)¶

Bases: Unpickler

python’s Unpickler extended to interpreter sessions and more types

_session = False¶

find_class(module, name)¶

Return an object from a specified module.

If necessary, the module will be imported. Subclasses may override this method (e.g. to restrict unpickling of arbitrary classes and functions).

This method is called whenever a class or a function object is needed. Both arguments passed are str objects.

load()¶

Load a pickle.

Read a pickled object representation from the open file object given in the constructor, and return the reconstituted object hierarchy specified therein.

settings = {'byref': False, 'fmode': 0, 'ignore': False, 'protocol': 4, 'recurse': False}¶

exception UnpicklingError¶: Bases: PickleError

exception UnpicklingWarning¶: Bases: PickleWarning, UnpicklingError

check(obj, *args, **kwds)¶

Check pickling of an object across another process.

python is the path to the python interpreter (defaults to sys.executable)

Set verbose=True to print the unpickled object in the other process.

Additional keyword arguments are as dumps() and loads().

citation()¶: print citation

copy(obj, *args, **kwds)¶

Use pickling to ‘copy’ an object (i.e. loads(dumps(obj))).

See dumps() and loads() for keyword arguments.

dump(obj, file, protocol=None, byref=None, fmode=None, recurse=None, **kwds)¶

Pickle an object to a file.

See dumps() for keyword arguments.

dump_module(filename=None, module=None, refimported=False, **kwds)¶

Pickle the current state of __main__ or another module to a file.

Save the contents of __main__ (e.g. from an interactive interpreter session), an imported module, or a module-type object (e.g. built with ModuleType), to a file. The pickled module can then be restored with the function load_module().

Parameters

filename (Optional[Union[str, PathLike]]) – a path-like object or a writable stream. If None (the default), write to a named file in a temporary directory.
module (Optional[Union[module, str]]) – a module object or the name of an importable module. If None (the default), __main__ is saved.
refimported (bool) – if True, all objects identified as having been imported into the module’s namespace are saved by reference. Note: this is similar but independent from dill.settings[`byref`], as refimported refers to virtually all imported objects, while byref only affects select objects.
**kwds – extra keyword arguments passed to Pickler().

Raises

PicklingError – if pickling fails.

Return type

None

Examples

Save current interpreter session state:

>>> import dill
>>> squared = lambda x: x*x
>>> dill.dump_module() # save state of __main__ to /tmp/session.pkl

Save the state of an imported/importable module:

>>> import dill
>>> import pox
>>> pox.plus_one = lambda x: x+1
>>> dill.dump_module('pox_session.pkl', module=pox)

Save the state of a non-importable, module-type object:

>>> import dill
>>> from types import ModuleType
>>> foo = ModuleType('foo')
>>> foo.values = [1,2,3]
>>> import math
>>> foo.sin = math.sin
>>> dill.dump_module('foo_session.pkl', module=foo, refimported=True)

Restore the state of the saved modules:

>>> import dill
>>> dill.load_module()
>>> squared(2)
4
>>> pox = dill.load_module('pox_session.pkl')
>>> pox.plus_one(1)
2
>>> foo = dill.load_module('foo_session.pkl')
>>> [foo.sin(x) for x in foo.values]
[0.8414709848078965, 0.9092974268256817, 0.1411200080598672]

Use refimported to save imported objects by reference:

>>> import dill
>>> from html.entities import html5
>>> type(html5), len(html5)
(dict, 2231)
>>> import io
>>> buf = io.BytesIO()
>>> dill.dump_module(buf) # saves __main__, with html5 saved by value
>>> len(buf.getvalue()) # pickle size in bytes
71665
>>> buf = io.BytesIO()
>>> dill.dump_module(buf, refimported=True) # html5 saved by reference
>>> len(buf.getvalue())
438

Changed in version 0.3.6: Function dump_session() was renamed to dump_module(). Parameters main and byref were renamed to module and refimported, respectively.

Note

Currently, dill.settings['byref'] and dill.settings['recurse'] don’t apply to this function.

dump_session(filename=None, main=None, byref=False, **kwds)¶

Pickle the current state of __main__ or another module to a file.

Save the contents of __main__ (e.g. from an interactive interpreter session), an imported module, or a module-type object (e.g. built with ModuleType), to a file. The pickled module can then be restored with the function load_module().

Parameters

filename – a path-like object or a writable stream. If None (the default), write to a named file in a temporary directory.
module – a module object or the name of an importable module. If None (the default), __main__ is saved.
refimported – if True, all objects identified as having been imported into the module’s namespace are saved by reference. Note: this is similar but independent from dill.settings[`byref`], as refimported refers to virtually all imported objects, while byref only affects select objects.
**kwds – extra keyword arguments passed to Pickler().

Raises

PicklingError – if pickling fails.

Examples

Save current interpreter session state:

>>> import dill
>>> squared = lambda x: x*x
>>> dill.dump_module() # save state of __main__ to /tmp/session.pkl

Save the state of an imported/importable module:

>>> import dill
>>> import pox
>>> pox.plus_one = lambda x: x+1
>>> dill.dump_module('pox_session.pkl', module=pox)

Save the state of a non-importable, module-type object:

>>> import dill
>>> from types import ModuleType
>>> foo = ModuleType('foo')
>>> foo.values = [1,2,3]
>>> import math
>>> foo.sin = math.sin
>>> dill.dump_module('foo_session.pkl', module=foo, refimported=True)

Restore the state of the saved modules:

>>> import dill
>>> dill.load_module()
>>> squared(2)
4
>>> pox = dill.load_module('pox_session.pkl')
>>> pox.plus_one(1)
2
>>> foo = dill.load_module('foo_session.pkl')
>>> [foo.sin(x) for x in foo.values]
[0.8414709848078965, 0.9092974268256817, 0.1411200080598672]

Use refimported to save imported objects by reference:

>>> import dill
>>> from html.entities import html5
>>> type(html5), len(html5)
(dict, 2231)
>>> import io
>>> buf = io.BytesIO()
>>> dill.dump_module(buf) # saves __main__, with html5 saved by value
>>> len(buf.getvalue()) # pickle size in bytes
71665
>>> buf = io.BytesIO()
>>> dill.dump_module(buf, refimported=True) # html5 saved by reference
>>> len(buf.getvalue())
438

Changed in version 0.3.6: Function dump_session() was renamed to dump_module(). Parameters main and byref were renamed to module and refimported, respectively.

Note

Currently, dill.settings['byref'] and dill.settings['recurse'] don’t apply to this function.

dumps(obj, protocol=None, byref=None, fmode=None, recurse=None, **kwds)¶

Pickle an object to a string.

protocol is the pickler protocol, as defined for Python pickle.

If byref=True, then dill behaves a lot more like pickle as certain objects (like modules) are pickled by reference as opposed to attempting to pickle the object itself.

If recurse=True, then objects referred to in the global dictionary are recursively traced and pickled, instead of the default behavior of attempting to store the entire global dictionary. This is needed for functions defined via exec().

fmode (HANDLE_FMODE, CONTENTS_FMODE, or FILE_FMODE) indicates how file handles will be pickled. For example, when pickling a data file handle for transfer to a remote compute service, FILE_FMODE will include the file contents in the pickle and cursor position so that a remote method can operate transparently on an object with an open file handle.

Default values for keyword arguments can be set in dill.settings.

extend(use_dill=True)¶

add (or remove) dill types to/from the pickle registry

by default, dill populates its types to pickle.Pickler.dispatch. Thus, all dill types are available upon calling 'import pickle'. To drop all dill types from the pickle dispatch, use_dill=False.

Parameters: use_dill (bool, default=True) – if True, extend the dispatch table.
Returns: None

license()¶: print license

load(file, ignore=None, **kwds)¶

Unpickle an object from a file.

See loads() for keyword arguments.

load_module(filename=None, module=None, **kwds)¶

Update the selected module (default is __main__) with the state saved at filename.

Restore a module to the state saved with dump_module(). The saved module can be __main__ (e.g. an interpreter session), an imported module, or a module-type object (e.g. created with ModuleType).

When restoring the state of a non-importable module-type object, the current instance of this module may be passed as the argument main. Otherwise, a new instance is created with ModuleType and returned.

Parameters

filename (Optional[Union[str, PathLike]]) – a path-like object or a readable stream. If None (the default), read from a named file in a temporary directory.
module (Optional[Union[module, str]]) – a module object or the name of an importable module; the module name and kind (i.e. imported or non-imported) must match the name and kind of the module stored at filename.
**kwds – extra keyword arguments passed to Unpickler().

Raises

UnpicklingError – if unpickling fails.
ValueError – if the argument main and module saved at filename are incompatible.

Returns

A module object, if the saved module is not __main__ or a module instance wasn’t provided with the argument main.

Return type

Optional[module]

Examples

Save the state of some modules:

>>> import dill
>>> squared = lambda x: x*x
>>> dill.dump_module() # save state of __main__ to /tmp/session.pkl
>>>
>>> import pox # an imported module
>>> pox.plus_one = lambda x: x+1
>>> dill.dump_module('pox_session.pkl', module=pox)
>>>
>>> from types import ModuleType
>>> foo = ModuleType('foo') # a module-type object
>>> foo.values = [1,2,3]
>>> import math
>>> foo.sin = math.sin
>>> dill.dump_module('foo_session.pkl', module=foo, refimported=True)

Restore the state of the interpreter:

>>> import dill
>>> dill.load_module() # updates __main__ from /tmp/session.pkl
>>> squared(2)
4

Load the saved state of an importable module:

>>> import dill
>>> pox = dill.load_module('pox_session.pkl')
>>> pox.plus_one(1)
2
>>> import sys
>>> pox in sys.modules.values()
True

Load the saved state of a non-importable module-type object:

>>> import dill
>>> foo = dill.load_module('foo_session.pkl')
>>> [foo.sin(x) for x in foo.values]
[0.8414709848078965, 0.9092974268256817, 0.1411200080598672]
>>> import math
>>> foo.sin is math.sin # foo.sin was saved by reference
True
>>> import sys
>>> foo in sys.modules.values()
False

Update the state of a non-importable module-type object:

>>> import dill
>>> from types import ModuleType
>>> foo = ModuleType('foo')
>>> foo.values = ['a','b']
>>> foo.sin = lambda x: x*x
>>> dill.load_module('foo_session.pkl', module=foo)
>>> [foo.sin(x) for x in foo.values]
[0.8414709848078965, 0.9092974268256817, 0.1411200080598672]

Changed in version 0.3.6: Function load_session() was renamed to load_module(). Parameter main was renamed to module.

dill package documentation¶

dill: serialize all of Python¶

About Dill¶

Major Features¶

Current Release¶

Development Version¶

Installation¶

Requirements¶

Basic Usage¶

More Information¶

Citation¶

Indices and tables¶